Abstract
Cell segmentation is one of the fundamental problems in biomedical image processing as it is often mandatory for the quantitative analysis of biological processes. Sometimes, a binary segmentation of the cells is not sufficient, for instance if biologists are interested in the appearance of specific cell parts. Such a setting requires multiple foreground classes, which can significantly increase the complexity of the segmentation task. This is especially the case if very fine structures need to be detected. Here, we propose a method for multi-class segmentation of Drosophila macrophages in in-vivo fluorescence microscopy images to segment complex cell structures such as the lamellipodium and filopodia. Our approach is based on a convolutional neural network, more specifically the U-net architecture. The network is trained using a loss function based on the F\(_1\)-measure which we have extended for multi-class scenarios to account for class imbalances in the image data. We compare the F\(_1\)-measure loss function to a weighted cross entropy loss and show that the CNN outperforms other segmentation approaches.
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References
Akram, S.U., Kannala, J., Eklund, L., Heikkilä, J.: Cell proposal network for microscopy image analysis. In: IEEE International Conference on Image Processing (ICIP), pp. 3199–3203 (2016)
Akram, S.U., Kannala, J., Eklund, L., Heikkilä, J.: Cell segmentation proposal network for microscopy image analysis. In: Proceedings of Deep Learning and Data Labeling for Medical Applications, pp. 21–29 (2016)
Aydin, A.S., Dubey, A., Dovrat, D., Aharoni, A., Shilkrot, R.: CNN based yeast cell segmentation in multi-modal fluorescent microscopy data. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 753–759 (2017)
Barry, D.J., Durkin, C.H., Abella, J.V., Way, M.: Open source software for quantification of cell migration, protrusions, and fluorescence intensities. J. Cell Biol. 209(1), 163–180 (2015)
Bergeest, J., Rohr, K.: Efficient globally optimal segmentation of cells in fluorescence microscopy images using level sets and convex energy functionals. Med. Image Anal. 16(7), 1436–1444 (2012)
Bernier-Latmani, J., Petrova, T.V.: High-resolution 3D analysis of mouse small-intestinal stroma. Nat. Protoc. 119(9), 1617–1629 (2016)
Bian, A., Scherzinger, A., Jiang, X.: An enhanced multi-label random walk for biomedical image segmentation using statistical seed generation. In: Proceedings of International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS), pp. 748–760 (2017)
Bredies, K., Wolinski, H.: An active-contour based algorithm for the automated segmentation of dense yeast populations on transmission microscopy images. Comput. Vis. Sci. 14(7), 341–352 (2011)
Castilla, C., Maska, M., Sorokin, D.V., Meijering, E., de Solorzano, C.O.: Segmentation of actin-stained 3D fluorescent cells with filopodial protrusions using convolutional neural networks. In: International Symposium on Biomedical Imaging (ISBI), pp. 413–417 (2018)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention (MICCAI), Part II, pp. 424–432 (2016)
Espinoza, E., Martinez, G., Frerichs, J., Scheper, T.: Cell cluster segmentation based on global and local thresholding for in-situ microscopy. In: Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 542–545 (2006)
Essa, E., Xie, X., Errington, R.J., White, N.S.: A multi-stage random forest classifier for phase contrast cell segmentation. In: Proceedings of International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3865–3868 (2015)
Hilsenbeck, O., et al.: FastER: a user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy. Bioinformatics 33(13), 2020–2028 (2017)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of ACM International Conference on Multimedia (MM), pp. 675–678 (2014)
Klemm, S., Scherzinger, A., Drees, D., Jiang, X.: Barista - a graphical tool for designing and training deep neural networks. CoRR abs/1802.04626 (2018). http://arxiv.org/abs/1802.04626
Lammel, U., et al.: The drosophila FHOD1-like formin Knittrig acts through Rok to promote stress fiber formation and directed macrophage migration during the cellular immune response. Development 14(1), 1366–1380 (2014)
Lee, D.H.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: ICML Workshop: Challenges in Representation Learning (WREPL) (2013)
Marcuzzo, M., Quelhas, P., Campilho, A., Mendonça, A.M., Campilho, A.: Automated arabidopsis plant root cell segmentation based on SVM classification and region merging. Comput. Biol. Med. 39(9), 785–793 (2009)
Milletari, F., Navab, N., Ahmadi, S.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: International Conference on 3D Vision (3DV), pp. 565–571 (2016)
Pastor-Pellicer, J., Zamora-Martínez, F., Boquera, S.E., Bleda, M.J.C.: F-measure as the error function to train neural networks. In: Proceedings of International Work-Conference on Artificial Neural Networks (IWANN), Part I, pp. 376–384 (2013)
Pinidiyaarachchi, A., Wählby, C.: Seeded watersheds for combined segmentation and tracking of cells. In: Roli, F., Vitulano, S. (eds.) Proceedings of Image Analysis and Processing (ICIAP), pp. 336–343 (2005)
Raza, S., Cheung, L., Epstein, D.B.A., Pelengaris, S., Khan, M., Rajpoot, N.M.: Mimo-net: a multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images. In: Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), pp. 337–340 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention (MICCAI), Part III, pp. 234–241 (2015)
Rüder, M., Nagel, B.M., Bogdan, S.: Analysis of cell shape and cell migration of Drosophila macrophages in vivo. In: Gautreau, A. (ed.) Cell Migration. MMB, vol. 1749, pp. 227–238. Springer, New York (2018). https://doi.org/10.1007/978-1-4939-7701-7_17
Sadanandan, S.K., Ranefall, P., Wählby, C.: Feature augmented deep neural networks for segmentation of cells. In: Proceedings of European Conference on Computer Vision (ECCV) Workshops, Part I, pp. 231–243 (2016)
Sander, M., Squarr, A.J., Risse, B., Jiang, X., Bogdan, S.: Drosophila pupal macrophages - a versatile tool for combined ex vivo and in vivo imaging of actin dynamics at high resolution. Eur. J. Cell Biol. 92(10–11), 349–354 (2013)
Scherzinger, A., Klemm, S., Berh, D., Jiang, X.: CNN-based background subtraction for long-term in-vial FIM imaging. In: Proceedings of International Conference on Computer Analysis of Images and Patterns (CAIP), Part I, pp. 359–371 (2017)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Valen, D.A.V., et al.: Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput. Biol. 12(11), e1005177 (2016)
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Scherzinger, A., Hugenroth, P., Rüder, M., Bogdan, S., Jiang, X. (2019). Multi-class Cell Segmentation Using CNNs with F\(_1\)-measure Loss Function. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_30
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DOI: https://doi.org/10.1007/978-3-030-12939-2_30
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